PROJECT SUMMARY Our ability to study the microbiomes is enabled by the same technologies that allow us to quantify the host physiological state at greater depth and precision, including advanced high-throughput sequencing for genomics and transcriptomics; mass spectrometry for metabolomics, proteomics, and lipidomics; and flow-cytometry for characterization of circulating cell populations. The integration of host and microbiome panomic data is the roadmap for future biomedical discoveries. One example of such studies is the Integrative Human Microbiome Project (iHMP), which is currently generating panomic data on microbes and their host environment in three different diseases (diabetes, irritable bowel disease, pre-term delivery). Lack of appropriate analytics for these data and a steep curve for their validation and adoption is a major concern for the community. The main challenge of inference in panomic-scale microbiome datasets is overcoming the ?curses of dimensionality?. Local causal learning has proven useful for making discoveries with high-dimensional data, while distance-based learning is a promising paradigm for multivariate data analysis. We are proposing to combine these to develop the next generation of panomic data analytics and make these tools available directly to the biomedical investigators. The aims of this project are: (1) Develop analytics for distance-based omnibus panomic integration; (2) Develop methodology for top-down distance-based sub-system interdependence learning. The overarching goal is to develop user-facing applications utilizing the methodologies in Aims 1 and 2 and apply those in several existing studies generating panomic data. The analytics, applications, and educational resources (case studies and tutorials) resulting from this project will enable the biomedical community to study panomic-scale datasets in a coherent and comprehensive way. The methods and tools resulting from this project will support new biomedical discoveries. ! !